Process-based Inference for Spatial Energetics Using Bayesian Predictive Stacking

TL;DR

This paper introduces a Bayesian inference framework for analyzing spatial-temporal health data from wearable GPS devices, using predictive stacking for fast, uncertainty-aware analysis of physical activity trajectories.

Contribution

It develops a novel Bayesian predictive stacking approach for spatial-temporal models, enabling rapid and probabilistic inference on GPS-based activity data.

Findings

Effective in simulation experiments

Applied successfully to real-world physical activity data

Provides uncertainty quantification in spatial energetics analysis

Abstract

Rapid developments in streaming data technologies have enabled real-time monitoring of human activity that can deliver high-resolution data on health variables over trajectories or paths carved out by subjects as they conduct their daily physical activities. Wearable devices, such as wrist-worn sensors that monitor gross motor activity, have become prevalent and have kindled the emerging field of "spatial energetics" in environmental health sciences. We devise a Bayesian inferential framework for analyzing such data while accounting for information available on specific spatial coordinates comprising a trajectory or path using a Global Positioning System (GPS) device embedded within the wearable device. We offer full probabilistic inference with uncertainty quantification using spatial-temporal process models adapted for data generated from "actigraph" units as the subject traverses a path or trajectory in their daily routine. Anticipating the need for fast inference for mobile health data, we pursue exact inference using conjugate Bayesian models and employ predictive stacking to assimilate inference across these individual models. This circumvents issues with iterative estimation algorithms such as Markov chain Monte Carlo. We devise Bayesian predictive stacking in this context for models that treat time as discrete epochs and that treat time as continuous. We illustrate our methods with simulation experiments and analysis of data from the Physical Activity through Sustainable Transport Approaches (PASTA-LA) study conducted by the Fielding School of Public Health at the University of California, Los Angeles.